Interpersonal Physiological Synchrony Predicts Group Cohesion

Abstract

A key emergent property of group social dynamic is synchrony-the coordination of actions, emotions, or physiological processes between group members. Despite this fact and the inherent nested structure of groups, little research has assessed physiological synchronization between group members from a multi-level perspective, thus limiting a full understanding of the dynamics between members. To address this gap of knowledge we re-analyzed a large dataset (N = 261) comprising physiological and psychological data that were collected in two laboratory studies that involved two different social group tasks. In both studies, following the group task, members reported their experience of group cohesion via questionnaires. We utilized a non-linear analysis method-multidimensional recurrence quantification analysis that allowed us to represent physiological synchronization in cardiological interbeat intervals between group members at the individual-level and at the group-level. We found that across studies and their conditions, the change in physiological synchrony from baseline to group interaction predicted a psychological sense of group cohesion. This result was evident both at the individual and the group levels and was not modified by the context of the interaction. The individual- and group-level effects were highly correlated. These results indicate that the relationship between synchrony and cohesion is a multilayered construct. We re-affirm the role of physiological synchrony for cohesion in groups. Future studies are needed to crystallize our understanding of the differences and similarities between synchrony at the individual-level and synchrony at the group level to illuminate under which conditions one of these levels has primacy, or how they interact.

Keywords: cohesion; group-level synchrony; individual-level-synchrony; physiological synchrony; recurrence quantification analysis.

FIGURE 1

The common phases of the two experiments – “drumming” and “decision making”.

FIGURE 2

Example recurrence plot for toy data. The dark squares in the plot indicate recurrence (identical/similar coordinates), while the white points indicate the absence of recurrence. Note that the recurrence plot (RP) is, by convention, rotated by 90° compared to the conventional display of a matrix, so that time runs from the lower-left to the upper-right of the plot. Recurrence measures for this plot are: %REC = 62.5%; %LAM = 90%; meanV = 3; maxV = 3. See text for how these values are computed.

FIGURE 3

An example of a multidimensional recurrence plot for one group participating in this study. Both the x-axis and y-axis represent the IBI time series. Blackened areas are recurrent points, and vertical lines are circled in red. To quantify the recurrence rate (REC%), we calculate the percentage of recurrent points across the matrix. Further, to identify recurrence patterns in time, we use LAM%, the rate of recurrence points with a vertical neighbor, and the length of the vertical lines (meanV and maxV).

FIGURE 4

Violin plots show the distribution of REC% of actual and pseudo triads during a group interaction.

FIGURE 5

Violin plots show the distribution of REC% of triads at baseline and during the group interaction.

FIGURE 6

Scatter plots and slopes for the IBI data during task minus baseline (triadic ΔSync) and the baseline data (IBI Sync at Baseline).

FIGURE 7

Two-way interaction plot, based on a random slopes and random intercept mixed model, predicting cohesion from ΔSync on triad level (A) and individual level (B) and on under different conditions (plotted as different colors). The positive correlations indicate that a positive change in IBI synchrony, from baseline to engagement in a group activity, predicts a stronger sense of cohesion among group members.